Overview

Dataset statistics

Number of variables11
Number of observations1000000
Missing cells0
Missing cells (%)0.0%
Duplicate rows5
Duplicate rows (%)< 0.1%
Total size in memory91.6 MiB
Average record size in memory96.0 B

Variable types

Numeric9
Categorical2

Alerts

storm has constant value ""Constant
storm phase has constant value ""Constant
Dataset has 5 (< 0.1%) duplicate rowsDuplicates
400kmDensity is highly overall correlated with SYM/H_INDEX_nT and 5 other fieldsHigh correlation
SYM/H_INDEX_nT is highly overall correlated with 400kmDensityHigh correlation
1-M_AE_nT is highly overall correlated with SYM/H_INDEX_nTHigh correlation
DAILY_SUNSPOT_NO_ is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
DAILY_F10.7_ is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
SOLAR_LYMAN-ALPHA_W/m^2 is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
mg_index (core to wing ratio (unitless)) is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
irradiance (W/m^2/nm) is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
SYM/H_INDEX_nT has 28270 (2.8%) zerosZeros
DAILY_SUNSPOT_NO_ has 159756 (16.0%) zerosZeros
d_diff has 16852 (1.7%) zerosZeros

Reproduction

Analysis started2023-02-24 21:40:37.498148
Analysis finished2023-02-24 21:41:25.311325
Duration47.81 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

400kmDensity
Real number (ℝ)

Distinct928645
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.714487 × 10-12
Minimum5.77655 × 10-16
Maximum2.539658 × 10-11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:41:25.385126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum5.77655 × 10-16
5-th percentile2.3343303 × 10-13
Q16.1467135 × 10-13
median1.202231 × 10-12
Q32.329736 × 10-12
95-th percentile4.8918047 × 10-12
Maximum2.539658 × 10-11
Range2.5396002 × 10-11
Interquartile range (IQR)1.7150646 × 10-12

Descriptive statistics

Standard deviation1.5605977 × 10-12
Coefficient of variation (CV)0.91024179
Kurtosis0
Mean1.714487 × 10-12
Median Absolute Deviation (MAD)7.1435555 × 10-13
Skewness0
Sum1.714487 × 10-6
Variance2.4354652 × 10-24
MonotonicityNot monotonic
2023-02-24T16:41:25.516748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.008403 × 10-127
 
< 0.1%
1.044812 × 10-126
 
< 0.1%
1.129793 × 10-126
 
< 0.1%
1.070292 × 10-125
 
< 0.1%
1.000263 × 10-125
 
< 0.1%
1.174717 × 10-125
 
< 0.1%
1.417164 × 10-125
 
< 0.1%
1.279007 × 10-125
 
< 0.1%
1.456168 × 10-125
 
< 0.1%
1.227943 × 10-125
 
< 0.1%
Other values (928635) 999946
> 99.9%
ValueCountFrequency (%)
5.77655 × 10-161
< 0.1%
1.083077 × 10-151
< 0.1%
1.105662 × 10-151
< 0.1%
1.211596 × 10-151
< 0.1%
1.513974 × 10-151
< 0.1%
1.948599 × 10-151
< 0.1%
2.19288 × 10-151
< 0.1%
2.271978 × 10-151
< 0.1%
2.274078 × 10-151
< 0.1%
3.031177 × 10-151
< 0.1%
ValueCountFrequency (%)
2.539658 × 10-111
< 0.1%
2.503598 × 10-111
< 0.1%
2.503457 × 10-111
< 0.1%
2.499609 × 10-111
< 0.1%
2.4308 × 10-111
< 0.1%
2.409958 × 10-111
< 0.1%
2.373049 × 10-111
< 0.1%
2.36808 × 10-111
< 0.1%
2.345695 × 10-111
< 0.1%
2.27503 × 10-111
< 0.1%

SYM/H_INDEX_nT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct596
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.631626
Minimum-490
Maximum151
Zeros28270
Zeros (%)2.8%
Negative641682
Negative (%)64.2%
Memory size15.3 MiB
2023-02-24T16:41:25.649421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-490
5-th percentile-41
Q1-16
median-5
Q33
95-th percentile15
Maximum151
Range641
Interquartile range (IQR)19

Descriptive statistics

Standard deviation22.718309
Coefficient of variation (CV)-2.6319849
Kurtosis64.361951
Mean-8.631626
Median Absolute Deviation (MAD)9
Skewness-5.1024966
Sum-8631626
Variance516.12158
MonotonicityNot monotonic
2023-02-24T16:41:25.771101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3 31970
 
3.2%
-1 31880
 
3.2%
-2 31879
 
3.2%
1 31416
 
3.1%
-4 30753
 
3.1%
3 29921
 
3.0%
2 29886
 
3.0%
-5 29577
 
3.0%
0 28270
 
2.8%
4 27644
 
2.8%
Other values (586) 696804
69.7%
ValueCountFrequency (%)
-490 2
< 0.1%
-488 1
< 0.1%
-487 2
< 0.1%
-486 1
< 0.1%
-485 2
< 0.1%
-484 1
< 0.1%
-483 1
< 0.1%
-482 1
< 0.1%
-481 1
< 0.1%
-480 2
< 0.1%
ValueCountFrequency (%)
151 1
< 0.1%
146 1
< 0.1%
143 2
< 0.1%
142 2
< 0.1%
136 1
< 0.1%
134 2
< 0.1%
132 2
< 0.1%
129 1
< 0.1%
128 2
< 0.1%
127 1
< 0.1%

1-M_AE_nT
Real number (ℝ)

Distinct2436
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean222.689
Minimum1
Maximum4192
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:41:25.902753image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18
Q144
median110
Q3312
95-th percentile773
Maximum4192
Range4191
Interquartile range (IQR)268

Descriptive statistics

Standard deviation264.76369
Coefficient of variation (CV)1.1889392
Kurtosis7.7055073
Mean222.689
Median Absolute Deviation (MAD)81
Skewness2.2379679
Sum2.22689 × 108
Variance70099.813
MonotonicityNot monotonic
2023-02-24T16:41:26.023394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 8206
 
0.8%
32 8203
 
0.8%
33 8176
 
0.8%
36 8144
 
0.8%
35 8063
 
0.8%
34 8049
 
0.8%
30 8014
 
0.8%
37 7989
 
0.8%
27 7943
 
0.8%
39 7935
 
0.8%
Other values (2426) 919278
91.9%
ValueCountFrequency (%)
1 20
 
< 0.1%
2 78
 
< 0.1%
3 265
 
< 0.1%
4 668
 
0.1%
5 1179
 
0.1%
6 1592
0.2%
7 1766
0.2%
8 2016
0.2%
9 2563
0.3%
10 3140
0.3%
ValueCountFrequency (%)
4192 1
< 0.1%
4174 1
< 0.1%
3763 1
< 0.1%
3719 1
< 0.1%
3708 1
< 0.1%
3698 1
< 0.1%
3680 1
< 0.1%
3642 1
< 0.1%
3625 1
< 0.1%
3583 1
< 0.1%

DAILY_SUNSPOT_NO_
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct177
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.85619
Minimum0
Maximum270
Zeros159756
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:41:26.141080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114
median48
Q386
95-th percentile149
Maximum270
Range270
Interquartile range (IQR)72

Descriptive statistics

Standard deviation51.683174
Coefficient of variation (CV)0.90901579
Kurtosis1.2968937
Mean56.85619
Median Absolute Deviation (MAD)35
Skewness1.1294623
Sum56856190
Variance2671.1505
MonotonicityNot monotonic
2023-02-24T16:41:26.269764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 159756
 
16.0%
13 36531
 
3.7%
15 18650
 
1.9%
14 16583
 
1.7%
12 15865
 
1.6%
27 14297
 
1.4%
26 13459
 
1.3%
18 12761
 
1.3%
65 12278
 
1.2%
19 11957
 
1.2%
Other values (167) 687863
68.8%
ValueCountFrequency (%)
0 159756
16.0%
6 2761
 
0.3%
7 169
 
< 0.1%
8 1466
 
0.1%
9 6892
 
0.7%
10 8459
 
0.8%
11 11107
 
1.1%
12 15865
 
1.6%
13 36531
 
3.7%
14 16583
 
1.7%
ValueCountFrequency (%)
270 1161
0.1%
252 1368
0.1%
250 2661
0.3%
248 1386
0.1%
247 1679
0.2%
234 924
 
0.1%
233 1377
0.1%
224 2541
0.3%
211 1393
0.1%
208 2432
0.2%

DAILY_F10.7_
Real number (ℝ)

Distinct565
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.698
Minimum65.9
Maximum999.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:41:26.393406image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum65.9
5-th percentile68
Q175.2
median94
Q3118.8
95-th percentile164.3
Maximum999.9
Range934
Interquartile range (IQR)43.6

Descriptive statistics

Standard deviation46.338316
Coefficient of variation (CV)0.4512095
Kurtosis194.00036
Mean102.698
Median Absolute Deviation (MAD)20.7
Skewness10.56995
Sum1.02698 × 108
Variance2147.2395
MonotonicityNot monotonic
2023-02-24T16:41:26.515107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69.3 10980
 
1.1%
76.9 8371
 
0.8%
67.3 6914
 
0.7%
79.3 6745
 
0.7%
70.9 6666
 
0.7%
70.3 6132
 
0.6%
73.1 5896
 
0.6%
105.2 5682
 
0.6%
69.5 5631
 
0.6%
67.2 5556
 
0.6%
Other values (555) 931427
93.1%
ValueCountFrequency (%)
65.9 1378
 
0.1%
66.2 238
 
< 0.1%
66.3 1221
 
0.1%
66.4 3192
0.3%
66.5 1398
 
0.1%
66.7 1396
 
0.1%
66.8 4054
0.4%
67 1874
 
0.2%
67.1 1911
 
0.2%
67.2 5556
0.6%
ValueCountFrequency (%)
999.9 1391
0.1%
275.4 1378
0.1%
270.9 1389
0.1%
267.6 1283
0.1%
246.9 1161
0.1%
242.6 1386
0.1%
232.8 1393
0.1%
225 347
 
< 0.1%
205.8 924
0.1%
199.8 575
0.1%

SOLAR_LYMAN-ALPHA_W/m^2
Real number (ℝ)

Distinct792
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0069769901
Minimum0.005926
Maximum0.009751
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:41:26.653710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.005926
5-th percentile0.006015
Q10.006298
median0.006908
Q30.0075
95-th percentile0.008423
Maximum0.009751
Range0.003825
Interquartile range (IQR)0.001202

Descriptive statistics

Standard deviation0.00077073886
Coefficient of variation (CV)0.11046868
Kurtosis-0.019784931
Mean0.0069769901
Median Absolute Deviation (MAD)0.000602
Skewness0.71385091
Sum6976.9901
Variance5.9403839 × 10-7
MonotonicityNot monotonic
2023-02-24T16:41:26.796364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.006031 5056
 
0.5%
0.006951 4577
 
0.5%
0.007045 4166
 
0.4%
0.008003 4146
 
0.4%
0.007089 4135
 
0.4%
0.006003 4127
 
0.4%
0.005953 4007
 
0.4%
0.00642 3992
 
0.4%
0.007731 3875
 
0.4%
0.007063 3826
 
0.4%
Other values (782) 958093
95.8%
ValueCountFrequency (%)
0.005926 1387
 
0.1%
0.005943 1385
 
0.1%
0.005946 351
 
< 0.1%
0.005953 4007
0.4%
0.005956 632
 
0.1%
0.005959 515
 
0.1%
0.005961 2193
0.2%
0.005963 529
 
0.1%
0.005969 753
 
0.1%
0.005972 1379
 
0.1%
ValueCountFrequency (%)
0.009751 1393
0.1%
0.00974 1386
0.1%
0.00972 347
 
< 0.1%
0.009662 1161
0.1%
0.0092 924
0.1%
0.009187 345
 
< 0.1%
0.009181 1158
0.1%
0.009111 1383
0.1%
0.009102 1394
0.1%
0.009093 575
0.1%
Distinct799
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26925347
Minimum0.26320001
Maximum0.28494
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:41:26.938973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.26320001
5-th percentile0.26394162
Q10.26517999
median0.26859
Q30.27263717
95-th percentile0.27742001
Maximum0.28494
Range0.02173999
Interquartile range (IQR)0.00745718

Descriptive statistics

Standard deviation0.0044551174
Coefficient of variation (CV)0.016546184
Kurtosis-0.23036109
Mean0.26925347
Median Absolute Deviation (MAD)0.00364041
Skewness0.69962924
Sum269253.47
Variance1.9848071 × 10-5
MonotonicityNot monotonic
2023-02-24T16:41:27.063641image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.26708001 6609
 
0.7%
0.27265999 5546
 
0.6%
0.26475999 4622
 
0.5%
0.26486999 4198
 
0.4%
0.26898 4179
 
0.4%
0.26536 4179
 
0.4%
0.26984999 4176
 
0.4%
0.26403001 4168
 
0.4%
0.26627001 4157
 
0.4%
0.26712999 4151
 
0.4%
Other values (789) 954015
95.4%
ValueCountFrequency (%)
0.26320001 1396
0.1%
0.26335999 1284
0.1%
0.26344001 927
 
0.1%
0.26348001 1394
0.1%
0.26352 1384
0.1%
0.26352999 1052
0.1%
0.26355001 292
 
< 0.1%
0.26357999 1272
0.1%
0.26359001 2553
0.3%
0.26363 232
 
< 0.1%
ValueCountFrequency (%)
0.28494 1386
0.1%
0.28485999 1393
0.1%
0.28428999 1161
0.1%
0.28426999 347
 
< 0.1%
0.28176999 924
0.1%
0.28143999 345
 
< 0.1%
0.28127 1158
0.1%
0.28095001 575
0.1%
0.28088 1378
0.1%
0.28086001 1383
0.1%

irradiance (W/m^2/nm)
Real number (ℝ)

Distinct971
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0056332466
Minimum0.0048813247
Maximum0.0073493496
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:41:27.186313image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0048813247
5-th percentile0.004941232
Q10.0051511205
median0.0055895182
Q30.005997099
95-th percentile0.0067077135
Maximum0.0073493496
Range0.0024680248
Interquartile range (IQR)0.0008459785

Descriptive statistics

Standard deviation0.000554341
Coefficient of variation (CV)0.098405242
Kurtosis-0.40873872
Mean0.0056332466
Median Absolute Deviation (MAD)0.00043197535
Skewness0.64117884
Sum5633.2466
Variance3.0729394 × 10-7
MonotonicityNot monotonic
2023-02-24T16:41:27.312974image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.004940496758 2487
 
0.2%
0.005260170903 2207
 
0.2%
0.005658049602 1628
 
0.2%
0.005648719613 1411
 
0.1%
0.006063474808 1410
 
0.1%
0.006116171833 1408
 
0.1%
0.00517245708 1408
 
0.1%
0.005752183031 1407
 
0.1%
0.004998461809 1407
 
0.1%
0.004994971212 1406
 
0.1%
Other values (961) 983821
98.4%
ValueCountFrequency (%)
0.004881324712 701
0.1%
0.004886395764 583
0.1%
0.004887722898 1329
0.1%
0.004896449856 1376
0.1%
0.004898921587 1051
0.1%
0.004900876433 696
0.1%
0.004904543981 1387
0.1%
0.004905090202 758
0.1%
0.004905119073 1221
0.1%
0.004905175883 1376
0.1%
ValueCountFrequency (%)
0.007349349558 1045
0.1%
0.00734248152 1392
0.1%
0.007301890757 1388
0.1%
0.007257604506 462
 
< 0.1%
0.007178029511 632
0.1%
0.007079309318 345
 
< 0.1%
0.007055485155 1394
0.1%
0.007023133337 1268
0.1%
0.006999286357 625
0.1%
0.006995057687 1385
0.1%

storm
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
1
1000000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1000000
100.0%

Length

2023-02-24T16:41:27.418691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-24T16:41:27.508452image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1000000
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1000000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1000000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1000000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1000000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1000000
100.0%

storm phase
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
1
1000000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1000000
100.0%

Length

2023-02-24T16:41:27.580260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-24T16:41:27.669022image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1000000
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1000000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1000000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1000000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1000000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1000000
100.0%

d_diff
Real number (ℝ)

Distinct822054
Distinct (%)82.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3585773 × 10-16
Minimum-1.1756252 × 10-11
Maximum1.0096702 × 10-11
Zeros16852
Zeros (%)1.7%
Negative479818
Negative (%)48.0%
Memory size15.3 MiB
2023-02-24T16:41:27.768756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1.1756252 × 10-11
5-th percentile-2.0319805 × 10-13
Q1-4.1665 × 10-14
median4.462 × 10-16
Q34.374555 × 10-14
95-th percentile1.9821815 × 10-13
Maximum1.0096702 × 10-11
Range2.1852954 × 10-11
Interquartile range (IQR)8.541055 × 10-14

Descriptive statistics

Standard deviation2.0589881 × 10-13
Coefficient of variation (CV)1515.5473
Kurtosis0
Mean1.3585773 × 10-16
Median Absolute Deviation (MAD)4.27038 × 10-14
Skewness0
Sum1.3585773 × 10-10
Variance4.2394321 × 10-26
MonotonicityNot monotonic
2023-02-24T16:41:27.891428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16852
 
1.7%
9.151 × 10-159
 
< 0.1%
-1.3312 × 10-149
 
< 0.1%
-4.03 × 10-169
 
< 0.1%
3.4701 × 10-149
 
< 0.1%
4.282 × 10-159
 
< 0.1%
3.0556 × 10-149
 
< 0.1%
2.0499 × 10-149
 
< 0.1%
1.0635 × 10-149
 
< 0.1%
2.4115 × 10-148
 
< 0.1%
Other values (822044) 983068
98.3%
ValueCountFrequency (%)
-1.1756252 × 10-111
< 0.1%
-1.163180901 × 10-111
< 0.1%
-9.659238 × 10-121
< 0.1%
-9.648661 × 10-121
< 0.1%
-9.4411153 × 10-121
< 0.1%
-8.729284 × 10-121
< 0.1%
-8.4922911 × 10-121
< 0.1%
-8.2091393 × 10-121
< 0.1%
-7.4875632 × 10-121
< 0.1%
-7.406176 × 10-121
< 0.1%
ValueCountFrequency (%)
1.0096702 × 10-111
< 0.1%
9.7292573 × 10-121
< 0.1%
9.4953338 × 10-121
< 0.1%
8.742143 × 10-121
< 0.1%
8.235121 × 10-121
< 0.1%
7.728122 × 10-121
< 0.1%
7.3576767 × 10-121
< 0.1%
7.170111 × 10-121
< 0.1%
7.166516 × 10-121
< 0.1%
6.896834 × 10-121
< 0.1%

Interactions

2023-02-24T16:41:21.962251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:06.916504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:08.768553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:10.603619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:12.406801image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:14.220948image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:16.021136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:17.916071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:19.792054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:22.162718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:07.131903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:08.967992image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:10.804083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:12.608260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:14.424403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:16.232572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:18.124512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:20.001495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:22.364177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:07.344337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:09.169453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:11.004549image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:12.813726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:14.631851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:16.452981image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:18.336944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:20.205947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:22.561652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:07.547789image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:09.379890image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:11.192074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:13.008190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:14.823338image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:16.659431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:18.539403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:20.403421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:22.760147image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:07.749250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:09.581354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:11.411460image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:13.203668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:15.018817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:16.869869image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:18.746851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:20.606876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:22.950610image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:07.946723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:09.779820image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:11.605940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:13.400144image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:15.210305image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:17.072324image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:18.951301image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:20.811332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:23.161047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:08.172118image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:09.992256image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:11.819369image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:13.615567image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:15.422737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:17.287749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:19.167722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:21.026753image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:23.357521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:08.373581image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:10.192720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:12.015844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:13.820021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:15.622202image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:17.501179image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:19.368187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:21.222230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:23.552002image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:08.575042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:10.397171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:12.208332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:14.022480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:15.822668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:17.708626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:19.575673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:41:21.766807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-02-24T16:41:27.993162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)d_diff
400kmDensity1.000-0.2580.3770.7620.8140.8430.8200.8580.050
SYM/H_INDEX_nT-0.2581.000-0.541-0.128-0.133-0.134-0.111-0.145-0.007
1-M_AE_nT0.377-0.5411.0000.2730.2920.2950.2560.3110.009
DAILY_SUNSPOT_NO_0.762-0.1280.2731.0000.9420.8970.8880.8840.008
DAILY_F10.7_0.814-0.1330.2920.9421.0000.9500.9360.9410.008
SOLAR_LYMAN-ALPHA_W/m^20.843-0.1340.2950.8970.9501.0000.9610.9880.008
mg_index (core to wing ratio (unitless))0.820-0.1110.2560.8880.9360.9611.0000.9460.006
irradiance (W/m^2/nm)0.858-0.1450.3110.8840.9410.9880.9461.0000.008
d_diff0.050-0.0070.0090.0080.0080.0080.0060.0081.000
2023-02-24T16:41:28.161705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
400kmDensity1.000-0.3590.3030.7050.5310.7860.7610.800NaNNaN0.066
SYM/H_INDEX_nT-0.3591.000-0.555-0.173-0.153-0.170-0.147-0.166NaNNaN-0.001
1-M_AE_nT0.303-0.5551.0000.2020.1380.2070.1810.214NaNNaN0.003
DAILY_SUNSPOT_NO_0.705-0.1730.2021.0000.6520.8890.8820.862NaNNaN0.000
DAILY_F10.7_0.531-0.1530.1380.6521.0000.6670.6810.645NaNNaN0.000
SOLAR_LYMAN-ALPHA_W/m^20.786-0.1700.2070.8890.6671.0000.9630.980NaNNaN0.000
mg_index (core to wing ratio (unitless))0.761-0.1470.1810.8820.6810.9631.0000.939NaNNaN0.000
irradiance (W/m^2/nm)0.800-0.1660.2140.8620.6450.9800.9391.000NaNNaN0.000
stormNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
storm phaseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
d_diff0.066-0.0010.0030.0000.0000.0000.0000.000NaNNaN1.000
2023-02-24T16:41:28.343193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
400kmDensity1.000-0.2580.3770.7620.8140.8430.8200.858NaNNaN0.050
SYM/H_INDEX_nT-0.2581.000-0.541-0.128-0.133-0.134-0.111-0.145NaNNaN-0.007
1-M_AE_nT0.377-0.5411.0000.2730.2920.2950.2560.311NaNNaN0.009
DAILY_SUNSPOT_NO_0.762-0.1280.2731.0000.9420.8970.8880.884NaNNaN0.008
DAILY_F10.7_0.814-0.1330.2920.9421.0000.9500.9360.941NaNNaN0.008
SOLAR_LYMAN-ALPHA_W/m^20.843-0.1340.2950.8970.9501.0000.9610.988NaNNaN0.008
mg_index (core to wing ratio (unitless))0.820-0.1110.2560.8880.9360.9611.0000.946NaNNaN0.006
irradiance (W/m^2/nm)0.858-0.1450.3110.8840.9410.9880.9461.000NaNNaN0.008
stormNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
storm phaseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
d_diff0.050-0.0070.0090.0080.0080.0080.0060.008NaNNaN1.000
2023-02-24T16:41:28.524706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
400kmDensity1.000-0.1760.2560.5630.6120.6440.6130.661NaNNaN0.035
SYM/H_INDEX_nT-0.1761.000-0.380-0.090-0.091-0.092-0.076-0.099NaNNaN-0.004
1-M_AE_nT0.256-0.3801.0000.1860.1980.1990.1710.210NaNNaN0.006
DAILY_SUNSPOT_NO_0.563-0.0900.1861.0000.7910.7230.7060.704NaNNaN0.005
DAILY_F10.7_0.612-0.0910.1980.7911.0000.8020.7740.785NaNNaN0.006
SOLAR_LYMAN-ALPHA_W/m^20.644-0.0920.1990.7230.8021.0000.8260.914NaNNaN0.005
mg_index (core to wing ratio (unitless))0.613-0.0760.1710.7060.7740.8261.0000.791NaNNaN0.004
irradiance (W/m^2/nm)0.661-0.0990.2100.7040.7850.9140.7911.000NaNNaN0.005
stormNaNNaNNaNNaNNaNNaNNaNNaN1.000NaNNaN
storm phaseNaNNaNNaNNaNNaNNaNNaNNaNNaN1.000NaN
d_diff0.035-0.0040.0060.0050.0060.0050.0040.005NaNNaN1.000
2023-02-24T16:41:28.707219image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)d_diff
400kmDensity1.0000.6180.3010.6320.5080.6930.6790.6920.263
SYM/H_INDEX_nT0.6181.0000.4890.4130.4500.3080.3150.2310.191
1-M_AE_nT0.3010.4891.0000.2290.1800.2010.2150.1920.159
DAILY_SUNSPOT_NO_0.6320.4130.2291.0000.7680.8880.8980.8520.117
DAILY_F10.7_0.5080.4500.1800.7681.0000.6910.6710.6030.093
SOLAR_LYMAN-ALPHA_W/m^20.6930.3080.2010.8880.6911.0000.9650.9690.114
mg_index (core to wing ratio (unitless))0.6790.3150.2150.8980.6710.9651.0000.9440.117
irradiance (W/m^2/nm)0.6920.2310.1920.8520.6030.9690.9441.0000.114
d_diff0.2630.1910.1590.1170.0930.1140.1170.1141.000

Missing values

2023-02-24T16:41:23.700634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-24T16:41:24.190322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
16896361.199438e-12-26.0321.014.076.60.0063900.2656900.005145116.586700e-14
22556881.850956e-12-1.038.0102.0136.70.0077230.2762580.00618311-8.386900e-14
44183188.326546e-13-5.081.033.086.40.0065350.2668200.00533711-4.046100e-14
12952866.058874e-12-4.0128.0110.0140.10.0080330.2749800.00650211-2.195070e-13
18702208.700889e-138.085.00.072.30.0062970.2662700.00517111-1.063343e-13
8033014.844031e-127.059.092.0136.20.0078960.2737000.006614113.277720e-13
137792.158208e-12-8.0141.078.0113.50.0073820.2703400.005913111.163600e-14
8253083.278183e-12-33.0398.0150.0146.40.0078550.2741100.006265113.896700e-14
44653453.113145e-1211.091.067.0106.30.0072550.2728360.005767110.000000e+00
28539105.393338e-13-8.0142.013.073.10.0062720.2647500.005086114.067570e-14
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
43477372.668083e-1212.019.0115.0125.90.0080210.2751000.00616511-1.716940e-13
7610991.263847e-120.041.069.0119.10.0074350.2724000.00587611-2.804830e-13
39107671.140860e-12-18.0421.00.070.70.0060310.2639600.004973112.190485e-13
17242193.971643e-13-8.09.010.070.60.0061280.2645100.00501511-8.440300e-15
25283801.001378e-12-19.0261.00.079.30.0064860.2678250.005260111.798740e-13
34754591.044937e-1212.049.09.073.10.0061360.2649130.005024116.314540e-14
43261474.023308e-129.0141.0103.0112.50.0077310.2713800.00611911-1.148280e-13
16890831.192533e-12-35.0267.014.076.60.0063900.2656900.00512711-4.704800e-14
8384541.460365e-12-14.0145.063.0113.40.0070450.2685700.00579511-2.995700e-14
8258053.610053e-12-12.039.0150.0146.40.0078550.2741100.006368112.137430e-13

Duplicate rows

Most frequently occurring

400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff# duplicates
04.713266e-13-11.035.00.069.00.0060650.2648790.004978110.02
16.571013e-13-13.025.017.079.30.0063810.2647900.005260110.02
29.629251e-133.054.013.074.60.0063970.2670640.005225110.02
31.242273e-1223.028.046.099.40.0064510.2665700.005257110.02
42.829962e-12-12.035.040.0100.40.0071790.2693900.005684110.02